Tutorials
EasyRegressor
Train default models in _DEFAULT_REGRESSORS
1import sklearn
2
3from easyfit.regressors import EasyRegressor
4
5# Load dataset
6diabetes_dataset = sklearn.datasets.load_diabetes(as_frame=True)
7diabetes_df = diabetes_dataset['frame']
8X = diabetes_df.drop('target', axis=1)
9y = diabetes_df['target']
10
11# Create and train model
12model = EasyRegressor()
13model.fit(X, y)
14
15# Print the scores of the models
16print(model.score(X, y))
Train default models in _DEFAULT_REGRESSORS with additional models
10# Create additional models_dict
11models_dict = {
12 "additonal_model" : sklearn.linear_model.LinearRegression()
13}
14# Create and train model
15model = EasyRegressor(models_dict=models_dict, include_defaults=True)
16model.fit(X, y)
17
18# Print the scores of the models
19print(model.score(X, y))
Train only additional models
10# Create additional models_dict
11models_dict = {
12 "additonal_model" : sklearn.linear_model.LinearRegression()
13}
14# Create and train model
15model = EasyRegressor(models_dict=models_dict, include_defaults=False)
16model.fit(X, y)
17
18# Print the scores of the models
19print(model.score(X, y))
EasyClassifier
Train default models in _DEFAULT_CLASSIFIERS
1import sklearn
2
3from easyfit.classifiers import EasyClassifier
4
5# Load dataset
6diabetes_dataset = sklearn.datasets.load_iris(as_frame=True)
7diabetes_df = diabetes_dataset['frame']
8X = diabetes_df.drop('target', axis=1)
9y = diabetes_df['target']
10
11# Create and train model
12model = EasyClassifier()
13model.fit(X, y)
14
15# Print the scores of the models
16print(model.score(X, y))
Train default models in _DEFAULT_CLASSIFIERS with additional models
10# Create additional models_dict
11models_dict = {
12 "additonal_model" : sklearn.linear_model.LogisticRegression()
13}
14# Create and train model
15model = EasyClassifier(models_dict=models_dict, include_defaults=True)
16model.fit(X, y)
17
18# Print the scores of the models
19print(model.score(X, y))
Train only additional models
10# Create additional models_dict
11models_dict = {
12 "additonal_model" : sklearn.linear_model.LogisticRegression()
13}
14# Create and train model
15model = EasyClassifier(models_dict=models_dict, include_defaults=False)
16model.fit(X, y)
17
18# Print the scores of the models
19print(model.score(X, y))